Exploring Large Language Models for Knowledge Graph Completion

Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph completion. We consider triples in knowledge graphs as text sequences and introduce an innovative framework called Knowledge Graph LLM (KG-LLM) to model these triples. Our technique employs entity and relation descriptions of a triple as prompts and utilizes the response for predictions. Experiments on various benchmark knowledge graphs demonstrate that our method attains state-of-the-art performance in tasks such as triple classification and relation prediction. We also find that fine-tuning relatively smaller models (e.g., LLaMA-7B, ChatGLM-6B) outperforms recent ChatGPT and GPT-4.Comment: Work in progres

Friend Ranking in Online Games via Pre-training Edge Transformers

Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper lost friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training a classifier for predicting lost players' return probability, but ignore feature information of (active) players and historical friend recall events. In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. Furthermore, we propose a novel Edge Transformer model and pre-train the model via masked auto-encoders. Our method achieves state-of-the-art results in the offline experiments and online A/B Tests of three Tencent games.Comment: Accepted by the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023

SHP-2 Promotes the Maturation of Oligodendrocyte Precursor Cells Through Akt and ERK1/2 Signaling In Vitro

Background: Oligodendrocyte precursor cells (OPCs) differentiate into oligodendrocytes (OLs), which are responsible for myelination. Myelin is essential for saltatory nerve conduction in the vertebrate nervous system. However, the molecular mechanisms of maturation and myelination by oligodendrocytes remain elusive. Methods and Findings: In the present study, we showed that maturation of oligodendrocytes was attenuated by sodium orthovanadate (a comprehensive inhibitor of tyrosine phosphatases) and PTPi IV (a specific inhibitor of SHP-2). It is also found that SHP-2 was persistently expressed during maturation process of OPCs. Down-regulation of endogenous SHP-2 led to impairment of oligodendrocytes maturation and this effect was triiodo-L-thyronine (T3) dependent. Furthermore, overexpression of SHP-2 was shown to promote maturation of oligodendrocytes. Finally, it has been identified that SHP-2 was involved in activation of Akt and extracellular-regulated kinases 1 and 2 (ERK1/2) induced by T3 in oligodendrocytes

Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty

Traditional classification algorithms require a large number of labelled examples from all the predefined classes, which is generally difficult and time-consuming to obtain. Furthermore, data uncertainty is prevalent in many real-world applications, such as sensor network, market analysis and medical diagnosis. In this article, we explore the issue of classification on uncertain data when only positive and unlabelled examples are available. We propose an algorithm to build naive Bayes classifier from positive and unlabelled examples with uncertainty. However, the algorithm requires the prior probability of positive class, and it is generally difficult for the user to provide this parameter in practice. Two approaches are proposed to avoid this user-specified parameter. One approach is to use a validation set to search for an appropriate value for this parameter, and the other is to estimate it directly. Our extensive experiments show that the two approaches can basically achieve satisfactory classification performance on uncertain data. In addition, our algorithm exploiting uncertainty in the dataset can potentially achieve better classification performance comparing to traditional naive Bayes which ignores uncertainty when handling uncertain data

Effect of solidification modes on the shape memory effect of cast Fe-Mn-Si-Cr-Ni shape memory alloys

We investigated the microstructures and shape memory effect (SME) of cast Fe-Mn-Si-Cr-Ni alloys with four solidification modes, i.e., austenitic, austenitic-ferritic, ferritic-austenitic, and ferritic modes. Ferritic-austenitic and ferritic modes introduced more stacking faults than austenitic and austenitic-ferritic ones, reducing stress-induced critical martensite stress. Thus, the former solidification modes displayed better SME than the latter solidification modes

Discrete element simulation of vibration compaction of slag subgrade

Abstract In this study, to improve the compaction quality and parameters of slag, discrete element models of irregular rock particles (10–60 mm) and circular soil particles (5 mm) were established based on on-site slag screening results. The motion of the vibratory roller was captured by coupling the roadbed model with the roller model in a simulation in which the roller vibrated and compacted the slag subgrade. The results indicated that (1) the best compaction was achieved when the small particle content was 40%, the medium particle content was 20%, and the large particle content was 40%. (2) When the slag was dominated by small rock particles, the optimum compaction frequency was 28 Hz, and when large rock particles dominated, the optimum compaction frequency was 33 Hz. (3) Rock particles were the primary particles that experienced stress in the vibration compaction, and the compressive force and displacement depended on the particle size. (4) The longitudinal and vertical displacements and rotation angles of the soil and rock particles were examined. The results of this study are conducive for advancing the understanding of slag compaction and improving the working efficiency and compaction quality of rollers

Mutations in the promoter region of methionine transporter gene metM (Rv3253c) confer para-aminosalicylic acid (PAS) resistance in Mycobacterium tuberculosis

ABSTRACTTuberculosis (TB) is a significant global public health threat. Despite the long-standing use of para-aminosalicylic acid (PAS) as a second-line anti-TB drug, its resistance mechanism remains unclear. In this study, we isolated 90 mutants of PAS-resistant Mycobacterium tuberculosis (MTB) H37Ra in 7H11 solid medium and performed whole-genome sequencing, gene overexpression, transcription level comparison and amino acid level determination in MTB, and promoter activity by β-galactosidase assays in Mycobacterium smegmatis to elucidate the mechanism of PAS resistance. Herein, we found that 47 of 90 (52.2%) PAS-resistant mutants had nine different mutations in the intergenic region of metM (Rv3253c) and Rv3254. Beta-galactosidase assays confirmed that mutations increased promoter activity only for metM but not Rv3254. Interestingly, overexpression of MetM or its M. smegmatis homolog (MSMEI_1796) either by its promoter in metM’s direction or by exogenous expression in MTB induced PAS resistance in a methionine-dependent manner. Therefore, drug susceptibility results for the metM promoter mutants can be misleading when using standard 7H10 or 7H9 medium, which lacks methionine. At the metabolism level, PAS treatment led to higher intracellular methionine levels in the mutants than the wild type, antagonizing PAS and conferring resistance. Furthermore, 12 different mutations in the metM promoter were identified in clinical MTB strains. In summary, we found a novel mechanism of PAS resistance in MTB. Mutations in the metM (Rv3253c) promoter upregulate metM transcription and elevate intracellular methionine, which antagonize PAS. Our findings shed new light on the mechanism of PAS resistance in MTB and highlight issues with the current PAS susceptibility culture medium.IMPORTANCEAlthough para-aminosalicylic acid (PAS) has been used to treat TB for more than 70 years, the understanding of PAS resistance mechanisms is still vague, living gaps in our ability to predict resistance and apply PAS effectively in clinical practice. This study aimed to address this knowledge gap by inducing in vitro PAS resistance in Mycobacterium tuberculosis (MTB) using 7H11 medium and discovering a new PAS resistance mechanism. Our research revealed that spontaneous mutations occurring in the promoter region of the methionine transporting gene, metM, can upregulate the expression of metM, resulting in increased intracellular transport of methionine and consequently high-level resistance of Mycobacterium tuberculosis to PAS. Notably, this resistance phenotype cannot be observed when using the commonly recommended 7H10 medium, possibly due to the lack of additional methionine supply compared with that when using the 7H11 medium. Mutations on the regulatory region of metM were also found in some clinical MTB strains. These findings may have important implications for the unexplained PAS resistance observed in clinical settings and provide insight into the failures of PAS treatment. Additionally, they underscore the importance of considering the choice of culture media when conducting drug susceptibility testing for MTB